Top Machine Learning Jobs in Warsaw

As an AI Research Engineer, you will develop and optimize vision-language models, managing the full model lifecycle from training to deployment, focusing on efficiency and innovation in multimodal architectures.

Lead full-cycle recruitment for AI/ML roles across Europe and LATAM: build pipelines, partner with hiring managers, screen candidates, manage interviews and offers, and improve AI-focused hiring processes.

Design and deploy production-grade systems integrating machine learning models into scalable software pipelines, while focusing on maintainability, performance, and collaboration with teams.

The Head of Education & Community at Siena is responsible for developing product education, onboarding programs, community events, and market education resources. This role involves creating curricula, conducting workshops, and supporting sales with training and demos while leveraging AI for teaching efficiency.

Design, build, deploy, and optimize production-grade machine learning systems for a proactive AI platform. Own projects end to end, including data curation, training, evaluation, inference, monitoring, troubleshooting, and iteration. Translate research into reliable systems, optimize latency and cost at scale, collaborate across research, engineering, and product, and mentor other engineers.

Enhance and operate a globally deployed recommender system by improving MLOps, CI/CD, experiment tracking, model deployment, scalable ML pipelines, monitoring, and observability. Provide technical guidance and mentorship while collaborating with data scientists, engineers, product managers, and business stakeholders to deliver reliable, reproducible, production-ready machine learning systems.

Lead design and implementation of ML/LLM systems for ingestion, knowledge extraction, retrieval, and reasoning. Build datasets, metrics, pipelines, agentic retrieval and RAG solutions. Establish MLOps, orchestration, observability, and experiment tracking. Collaborate on research agenda, shape ML architecture, and hire and grow the ML team.

The role involves designing and developing model evaluation pipelines, user studies for subjective evaluations, and automated dashboards to report results, alongside leading the evaluation team and communicating with other teams to improve model performance.

Lead and grow a ~50-person ML organization, prioritize high-impact AI projects, set ML technical direction and standards, ensure delivery and measurable business value, and collaborate across products to translate AI research into product innovations.

Conduct applied AI/ML research to solve home design challenges, work with raw data, test hypotheses, and build prototypes/MVP solutions.

The Senior ML Data Engineer will enhance data workflows, collaborate with teams to build datasets, influence data strategies, and implement pipelines for effective data usage in AI/ML products.

Develop, deploy, and maintain scalable AI/ML pipelines and production models, including LLMs and generative AI. Optimize model performance, latency, and cost across AWS, GCP, and Azure. Apply MLOps practices such as CI/CD, versioning, monitoring, logging, and maintenance. Collaborate with data scientists, software engineers, solution architects, and cross-functional teams to integrate AI capabilities into products and workflows.

You will design and optimize LLM-based machine learning solutions, focusing on natural language processing, deep learning methods, and inferencing pipelines.

Design, build, and deploy production-grade ML and Generative AI solutions. Develop end-to-end pipelines, integrate LLMs, implement MLOps/CI-CD, monitor models, support releases, and collaborate with cross-functional teams on cloud-native AI platforms.

Lead end-to-end ML work: define product problems as ML tasks, prototype and ship models (ranking, retrieval, LLM agents), build evaluation and monitoring, and measure marketplace impact (bookings, retention, GMV). Collaborate with engineering, product, and growth to deploy production models and improve LLM reliability.

The role involves implementing AI features, improving retrieval quality, collaborating with teams, and building backend APIs for various projects.

Build and productionize large-scale TTS and adjacent speech models end-to-end: model architecture, training, alignment, evaluation, data curation, distributed GPU scaling, latency/cost profiling, tooling, and safety/mitigation for speech systems.

Senior technical lead embedding with customer teams to design, architect, and deliver production-grade AI/ML solutions. Drive model optimization, high-performance training (TPUs), efficient serving (vLLM), MLOps, agentic systems, GDPR-compliant private AI, ROI and cost optimization, pre-sales technical support, mentoring, and external thought leadership.

The ML Ops Engineer will build and operate scalable ML inference platforms, focusing on model serving infrastructure and deployment pipelines for AI applications.

Design, train, and deploy retrieval, reranking, and relevance ML models for a production agent-native search platform. Build embedding-based indexing and large-scale retrieval systems, define evaluation metrics and pipelines, optimize latency/quality/cost trade-offs, and collaborate with engineers to integrate models into 24x7 production services.

Lead benchmarking and profiling of GPU platforms for ML/AI workloads. Profile system and kernel-level GPU performance, debug and optimize ML training and inference, perform acceptance testing of GPU clusters, run experiments across GPU configurations and interconnects, and develop tooling and dashboards to visualize performance and bottlenecks.

Lead the company’s ML/AI platform from experimentation through production, including SageMaker training infrastructure, model serving, inference pipelines, MLOps, and Java microservice integrations. Set AI/ML technical direction, develop GenAI and LLM retrieval and agent architectures, establish evaluation and safety guardrails, and partner with data science, DevOps, product, and engineering leadership on scalable ML roadmaps.

The Senior AI/ML Engineer will design and deploy AI systems for business impact, lead MLOps practices, and collaborate with teams to drive AI solutions.

The Senior Machine Learning Engineer will design and implement ML pipelines, optimize LLM performance, and collaborate on AI infrastructure for cost-efficient solutions.

Lead the technical vision, architecture, and delivery of large-scale recommendation and personalization systems. Build scalable retrieval, ranking, and real-time pipelines, own ML infrastructure and MLOps, design evaluation and A/B testing, mentor ML engineers, and collaborate with product and engineering to align models with business metrics.